Search Results (506)

Maintaining reliable communication while ensuring collision-free motion is a central challenge for mobile relay robots operating in indoor disaster environments, where abrupt non-line-of-sight (NLOS) degradation and narrow structural bottlenecks can severely disrupt multi-hop connectivity. To address this problem, this paper proposes a Gaussian Process-Driven Adaptive Tube Model Predictive Control (GP-ATMPC) framework for communication-preserving relay navigation. Gaussian process regression (GPR) is used to construct a probabilistic spatial radio map from sparse received signal strength indicator (RSSI) measurements, providing both the predicted channel mean and its uncertainty over unvisited regions. Motion uncertainty is represented by an adaptive ellipsoidal error tube whose radius varies with translational motion, angular motion, and localization uncertainty. Based on this tube model, both obstacle and communication constraints are tightened over the full closed-loop state tube via a tube-tightened lower confidence bound (LCB) that jointly accounts for radio-prediction and motion-tracking uncertainty. Across two indoor disaster environments and 50 Monte Carlo runs each, the proposed method attains the highest connectivity satisfaction rate among controllers that preserve a safe motion margin, with significantly fewer end-to-end connectivity violations than nominal and heuristic adaptive-margin MPC by a paired Wilcoxon test, while maintaining millisecond-level online solve times. A reactive connectivity-first baseline reaches slightly higher raw connectivity but at three to four times the near-collision rate and without feasibility or stability guarantees. The radio-prediction layer is further validated in a higher-fidelity Gazebo environment and on real indoor RSSI measurements, where it reconstructs the measured channel with a mean absolute error of about 2.1 dB. These results indicate that coupling spatial radio prediction with adaptive tube-based robust control provides an effective framework for resilient communication-aware relay navigation in degraded indoor environments. Full article

This article presents a simple, original data augmentation algorithm for Received Signal Strength Indicator (RSSI), dedicated to indoor localization systems. The aim of the research was to develop a synthetic data generation method to serve as a regularization technique, making models more robust against measurement noise. The proposed approach combines propagation modeling using polynomial regression with the individual statistical characteristics of each Access Point (AP), accounting for signal fluctuations and a probabilistic signal outage mechanism. The effectiveness of the proposed solution was experimentally verified by evaluating K-NN and MLP neural network models in both classification and regression variants. The study was conducted on datasets with different measurement grid granularities, demonstrating the algorithm’s ability to improve the generalization properties of estimators, even with a limited number of samples in the training set. The results showed that the use of augmentation reduced the Mean Absolute Error (MAE) by an average of approximately 20% for the dense training set and about 17% for the sparse set. Within the evaluated test environment, models trained on the augmented sparse measurement grid, which contained 67% fewer physical calibration points (30 points compared to the dense grid’s 92), reached a precision comparable to models trained on the dense real-world dataset. Analysis of histograms and Cumulative Distribution Functions (CDF) of the error confirmed the preservation of the signal’s statistical integrity and the effective mitigation of gross errors. The proposed solution constitutes an efficient and easy-to-implement alternative to complex generative models (e.g., GANs). These findings serve as a successful proof-of-concept and pilot study, laying the foundation for further development and validation in larger, more complex spatial environments. Full article

Remote supervision of agricultural robots depends on continuous interpretation of robot status and wireless link quality. In smart greenhouses, crop canopies, metallic frames, cultivation rows, and non-line-of-sight propagation can cause intermittent packet loss and RSSI attenuation. Treating such transient degradation as immediate communication failure can interrupt robot operation unnecessarily, whereas delayed recognition of persistent loss can compromise safety. This study proposes a probabilistic communication-state inference method for remotely supervised agricultural robots. The robot-to-gateway wireless link is represented by three states: normal, degraded, and failure. The degraded state acts as an uncertainty buffer that preserves recoverable degradation before failure escalation. Packet reception ratio, received signal strength, and trajectory-derived context are used to update state probabilities through a bounded transition mechanism. Field experiments with a mobile agricultural robot in a smart greenhouse showed an accuracy of $0.915\pm 0.007$ and a macro F1-score of $0.907\pm 0.008$, while reducing the premature failure rate to $18.0\pm 1.4\%$. Comparisons with threshold-based, moving-average, and adapted WSN fault-detection baselines, including a FedLSTM-inspired baseline, showed that binary fault-detection logic cannot explicitly preserve recoverable degraded communication intervals. The results indicate that probabilistic degradation modeling supports communication-aware remote supervision by distinguishing transient degradation from failure-level communication loss. Full article

Accurate path-loss models are essential for planning reliable wireless networks in underground mines, yet existing characterization studies rely on specialized channel sounders and vector network analyzers costing tens of thousands of dollars, placing them beyond the reach of most mine operators. This paper demonstrates that LoRa transceivers costing approximately US $15 per node can serve as a self-contained path-loss measurement instrument, logging the received signal strength indicator (RSSI) and signal-to-noise ratio (SNR) directly to a CSV file over a standard USB serial connection. A measurement campaign conducted at the Missouri S&T Experimental Mine on 31 March 2026 collected 4801 packets across four distinct underground canonical primitives: straight tunnel, T-junction, vertical shaft, and post-bend NLoS gallery at distances of 5 to 60 m using Waveshare Pico-LoRa-SX1262 boards operating at 915 MHz. The results reveal a pronounced two-zone propagation structure, including a line-of-sight (LoS) zone with a negative path-loss exponent of −0.34, confirming tunnel waveguide gain up to 25 m, followed by a steep NLoS zone with an exponent of 13.0 after a 24.0 dB bend diffraction loss. Environment-specific measurements quantify a 5.5 dB junction excess loss and a 29.5 dB shaft excess loss relative to a straight-tunnel reference. Spreading factor sensitivity tests across SF7, SF9, and SF12 confirm that RSSI measurements are consistent to within 2 dB across all SFs, validating the measurement methodology. The resulting four-zone path-loss model provides mine network planners with parameters sufficient for LoRa link budget design and relay node placement without any specialized RF instrumentation. Full article

Prestressed steel strands transfer structural loads through complex anchorage systems. During through-anchorage ultrasonic guided-wave inspection, strong reflections generated in the anchorage segment may obscure defect-related echoes and create blind zones in the received signals. This study investigates the generation mechanisms of these anchorage-induced reflections and evaluates the relative roles of stress-induced acoustoelastic impedance variation and load-dependent interfacial contact evolution. An acoustoelastic finite element model is first used to estimate the reflection contribution caused by stress concentration alone. The results show that the stress-induced reflection remains weak, with the reflection coefficient remaining below 0.0125 even at 80% of the ultimate tensile strength. A sensitivity-based equivalent spring-contact model is then employed to examine whether effective strand–wedge and wedge–anchorage interfacial stiffness variations can generate anchorage reflections with comparable order of magnitude and load-dependent trends. The contact-based model produces much stronger reflections, and roughness-sensitivity analysis indicates that the load-dependent trend is not governed by a single nominal roughness assumption. Multi-specimen stepwise tensioning experiments show repeatable load-dependent reflection trends at both 80 kHz and 240 kHz. The results therefore suggest that, within the investigated geometry and loading range, interfacial contact evolution is a more plausible dominant contributor to anchorage-induced guided-wave reflections than stress-induced acoustoelastic impedance variation. This work focuses on the physical origin of anchorage reflections and provides a mechanistic basis for interpreting anchorage-induced interference in future through-anchorage defect detection. Full article

Dense Long Range (LoRa) networks suffer from packet loss when many end devices contend for the same unlicensed channel. Channel activity detection (CAD) can miss weak or cross-spreading-factor (cross-SF) transmissions, while a uniform carrier sense multiple access with collision avoidance (CSMA/CA) backoff rule ignores the different time-on-air (ToA) costs of SF7–SF12 packets. To address these two coupled problems, this paper proposes an interference-limit-aware CSMA protocol (ILA-CSMA). ILA-CSMA first combines CAD with an instantaneous received signal strength indicator (RSSI) test derived from the residual interference tolerance of the selected spreading factor, and then scales the contention window according to normalized ToA. The protocol is implemented in the Framework for LoRa (FLoRa), an OMNeT++-based LoRa network simulator, and is evaluated for networks with 100–2000 nodes. Compared with Pure ALOHA, Slotted ALOHA, standard CSMA/CA, and two ablation variants, ILA-CSMA improves dense-network access by jointly reducing hidden collisions and airtime imbalance. In the 2000-node case, it increases the packet delivery ratio (PDR) by about 20 percentage points relative to standard CSMA/CA, keeps the Jain fairness index (JFI) above the 0.85 reference line, reduces the energy consumed per successful packet to 22% of the standard CSMA/CA value, and reduces conditional average packet delay from 18.5 s to 8.2 s. These results show that interference-aware sensing and ToA-aware backoff can improve large-scale LoRa access under the evaluated simulation conditions. Full article

In this work, we explore resilient cooperative localisation for electric vehicles subject to the hybrid attack of gradual global navigation satellite system (GNSS) drag-off spoofing along with received signal strength indicator (RSSI) jamming. In order to mitigate such attacks, a deep learning-based physical-layer security approach is presented. The presented approach includes a long short-term memory (LSTM) detector for attack detection, a regression-based RSSI signal purifier, and a cooperative fusion scheme, which decreases the dependence on the GNSS branch in case of attack detection. The proposed approach is validated via the Berlin Vehicle-to-Everything (V2X) dataset with respect to six scenarios, including benign GNSS-only and cooperative localisation, attacked localisation without defence, and attacked localisation with physical-layer security support. According to the experimental evaluation results, the considered hybrid attack significantly impacts the localisation accuracy, leading to an increase in the GNSS-only localisation error to root mean square error (RMSE) = 149.93 m, mean absolute error (MAE) = 129.81 m, and maximum error = 259.62 m. At the same time, the proposed cooperative localisation with physical-layer security decreases the attacked cooperative localisation error to RMSE = 4.00 m, MAE = 3.51 m, and maximum error = 12.01 m. Full article

To address the sharp degradation in positioning accuracy and the lack of robustness of received signal strength indication (RSSI)-based indoor localization methods when both the reference RSSI and path-loss exponent are mismatched, a Differential Iterative Joint Estimation (DIJE) localization method is proposed in this paper. The proposed method first employs a differential model to eliminate the uncertainty caused by reference RSSI, transforming the maximum likelihood estimation (MLE) problem into a matrix eigenvalue problem to enable fast and high-accuracy target position estimation. Additionally, an alternating iterative optimization framework for target position and path-loss exponent is constructed to achieve adaptive joint estimation of model parameters and target coordinates, effectively suppressing localization performance degradation induced by parameter mismatch. In this paper, the Cramér–Rao Lower Bound (CRLB) under the dual-parameter uncertainty scenario is derived as a theoretical performance benchmark, and both simulation experiments and public real-world datasets are used to validate the method’s performance. The results demonstrate that the DIJE method can approach the theoretical limit under varying noise levels, access point (AP) densities, and complex indoor environments. Compared with classical algorithms such as RSDPE, MLE-TLLS, SOCP3, and LCJE, the DIJE method exhibits significant advantages in localization accuracy, robustness, and adaptability to initial parameters, and can meet the engineering requirements of high-accuracy and low-latency real-time indoor localization. Full article

Near-bit multi-parameter MWD (measurement while drilling) is a key technology for achieving precise and efficient directional drilling in underground and tunnel engineering. The near-bit multi-parameter MWD method was studied, and a “center + side wall” distributed measurement scheme was proposed, based on an analysis of special application scenarios in underground and tunnel engineering. The transmission characteristics of Bluetooth wireless signals in water were investigated. An analysis of the underwater Bluetooth signal link was conducted. When the transmission distance is 100 mm, the received signal strength is −17.5 dBm, and the link margin is 69.5 dB. Wireless Bluetooth was used to transmit the near-bit data. A Bluetooth wireless communication simulation model was established using ANSYS software, and the influence of transmission power, transmission medium, and transmission distance on the Bluetooth signal strength was analyzed. The results indicate that: (1) the received signal strength increases with transmission power, and appropriately increasing the transmission power can improve the effect of Bluetooth wireless communication and extend the communication distance. (2) When the transmission medium is water, the received signal is unstable, and the echo loss curve shows a high and low oscillation form, presenting a frequency shift feature; when the transmission medium is air, the received signal is relatively stable, and the echo loss curve shows a parabolic form. The echo loss of Bluetooth wireless signal in water transmission is significantly higher than that in air transmission, indicating that the Bluetooth signal attenuates more rapidly when transmitted in water. (3) When the transmission distance increases near the optimal transmission frequency of 2.4 GHz, the echo loss increases accordingly, and the received signal strength of the wireless receiving module gradually decreases. The theoretical analysis, simulation, and indoor test results are in good agreement. The reasonable Bluetooth transmission power is 1 mW, and the transmission distance is 100 mm. After completing the overall scheme design and simulation analysis optimization, the structure, circuit, and program development were carried out, and the near-bit multi-parameter MWD device was developed. A laboratory water supply test was conducted, and the power supply, collection, and wireless transmission were all normal. A drilling test was carried out at an underground engineering of a coal mine in Wuhai City, achieving a drilling depth of 2328 m. A continuous and stable collection of various parameters such as WOB (weight on bit), torque, rotation speed, vibration, and gamma was carried out. A wireless transmission channel for near-bit data was established across the screw drilling tool. It can provide key technical support for the research and development of near-bit MWD in underground and tunnel engineering. Full article

Radio Frequency Identification (RFID) is an emerging technology in Industry 4.0 for low-cost logistics, yet direction and orientation estimation typically requires multiple antennas, and robustness under industrial multipath fading, operator variability, and signal fragmentation has not been evaluated. To address this gap, this study proposes a single-antenna RFID system that evaluated thirteen architectures spanning unsupervised methods (clustering algorithms) and supervised methods (classical machine learning, deep learning, and hybrid architectures) on Received Signal Strength Indicator (RSSI) and phase time-series reconstructed through a pipeline of Savitzky–Golay smoothing, phase unwrapping, and cubic spline resampling to $N$ = 50–300 samples, preserving signal morphology across variable-length RFID passes. The system further incorporates a physics-informed augmentation strategy that encodes multipath fading, distance variation, and fragmentation into synthetic training samples for cross-domain generalization without hardware modification. In controlled laboratory experiments, both direction and orientation tasks achieved $>$99.5% accuracy, while direction tracking was additionally validated on an industrial shop floor under varying distances, Non-Line-of-Sight (NLoS) occlusions, and signal fragmentation. Zero-shot transfer caused accuracy to degrade to near-chance levels for several configurations, confirming a pronounced domain gap. Domain adaptation with XGBoost recovered direction accuracy to $>$97% under severe fragmentation under NLoS conditions, with an inference latency of ≈150 $\mu$s. Under domain-adapted shop floor conditions, direction accuracy exceeded the 75–92% reported in prior single-antenna laboratory studies, suggesting that physics-informed domain adaptation is a promising approach for single-antenna RFID tracking in Industrial Internet of Things (IIoT) logistics environments. Full article

To address tracking accuracy degradation caused by noise in sensor observations, a maneuvering target tracking algorithm based on an improved Received Signal Strength Indicator (RSSI) ranging model is proposed for Wireless Sensor Networks (WSNs). The traditional deterministic ranging model is replaced by a backpropagation neural network optimized via the Osprey Optimization Algorithm (OOA-BP), which directly maps noisy RSSI measurements to precise physical distances. Filtering and tracking are executed using an Extended Kalman Filter (EKF) combined with a uniform circular motion model, demonstrating the robustness of the observation model across dynamic predictions. Simulation results validate the efficacy of the proposed framework. In the distance estimation phase, the OOA-BP model reduces the average ranging error to 0.04 m. During dynamic tracking, the integrated OOA-BP-EKF architecture demonstrates superior tracking performance compared to standard frameworks, reducing the Root Mean Square Error (RMSE) by 15.33% and 59.89% compared to GA-BP and standard BP algorithms, respectively. Full article

Accurate impact localization on spacecraft structural panels subjected to contact loading by on-orbit servicing robots is critical for real-time structural health monitoring (SHM), yet remains challenging due to heterogeneous elastic wave propagation in complex aluminum structures with stiffener ribs and bonded joints. Conventional Received Signal Strength Indicator (RSSI)-based weighted centroid methods rely on fixed path-loss exponents that cannot accommodate spatially varying wave attenuation, resulting in position-dependent localization errors that worsen significantly near structural discontinuities. This paper proposes a Crow Search Algorithm (CSA)-optimized adaptive weighted centroid algorithm using distributed Fiber Bragg Grating (FBG) sensors, featuring three principal innovations: (i) a novel FBG wavelength-shift-to-RSSI amplitude mapping derived from elastic wave attenuation theory, bridging optical fiber sensing with centroid localization; (ii) per-event online weight optimization via CSA that adapts sensor contributions to each individual impact’s strain-wave signature; and (iii) a multi-objective fitness function simultaneously optimizing localization accuracy, noise robustness, and temporal consistency. The proposed method is validated across 200 impact events distributed over five representative positions on a 1 m³ Al6061 satellite-like structure with 64 FBG sensors (8 × 8 grid, 125 mm pitch), under three Gaussian noise levels (σ = 1%, 3%, 5% of signal RMS), and benchmarked against classical weighted centroid (WC), PSO-WC, GA-WC, DE-WC, and GWO-WC using paired t-tests (p < 0.01). CSA-WC achieves a mean localization error of 4.63 mm—an 83.29% improvement over classical WC and the lowest error among all five compared algorithms—with an average computation time of 0.14 s per event, satisfying real-time monitoring requirements. Full article

The quality of Global Navigation Satellite System (GNSS) observations on smartphones is highly susceptible to multipath and non-line-of-sight (NLOS) effects in urban environments, resulting in complex and highly variable observation errors. These challenges highlight the necessity of a reliable stochastic model to ensure robust and unbiased parameter estimation. However, conventional empirical stochastic models, such as elevation-dependent or signal-to-noise ratio (SNR)-based weighting schemes, are often insufficient to capture the rapidly changing stochastic behavior of observations in dense urban environments. To overcome this limitation, an adaptive GNSS stochastic model based on a deep neural network (DNN) is developed by integrating SNR, satellite elevation angle, and post-fit pseudorange residuals, which provide a strong indicator of observation quality and environmental context. Specifically, a fully connected DNN is designed to use SNR, satellite elevation angle, and post-fit pseudorange residual as input features, representing signal strength, satellite geometry, and residual information, respectively, and to learn their nonlinear relationship with measurement uncertainty. The network output is then used to adaptively update the diagonal elements of the measurement noise covariance matrix, thereby realizing epoch-wise adaptive weighting within the Kalman filtering process. The proposed DNN-based stochastic model, together with several conventional models, was evaluated using GNSS observations collected by a low-cost u-blox ZED-F9P receiver (u-blox AG, Thalwil, Switzerland) and a Samsung Galaxy S21+ smartphone (Samsung Electronics Co., Ltd., Suwon, Republic of Korea) during vehicle experiments in dense urban canyons. The code-based single point positioning (SPP) results demonstrate that the DNN-based model consistently outperforms traditional stochastic models under both open-sky and urban conditions. The improvement is particularly pronounced for smartphone observations in severely obstructed environments. The proposed DNN-based model reduces the 3D RMSE from 14.25 m, 13.68 m, and 13.05 m, obtained with the elevation-, SNR-, and integrated elevation–SNR-based models, respectively, to 8.94 m, representing an improvement of approximately 35%. A similar improvement is observed for the u-blox ZED-F9P receiver, where the 3D RMSE decreases from 5.71 m, 4.69 m, and 5.15 m to 3.10 m. These results suggest the effectiveness of the proposed DNN-based stochastic model in mitigating complex observation errors and improving positioning accuracy, providing a promising solution for reliable positioning of low-cost GNSS receivers in challenging urban environments. Full article

Indoor localization in GNSS-denied environments remains a significant challenge due to the low sampling frequency and high variability of wireless signal measurements. This paper presents a wireless communication-based indoor localization method that integrates Wi-Fi received signal strength indication (RSSI) measurements with optical initialization and inertial sensor fusion. The proposed approach eliminates the need for labor-intensive fingerprinting and specialized infrastructure by leveraging existing Wi-Fi networks. Optical pose estimation using ArUco markers provides accurate initial position and orientation, enabling alignment between sensor coordinate systems and reducing inertial drift. During tracking, inertial measurements compensate for motion between sparse Wi-Fi observations by virtually translating historical RSSI samples, allowing statistically consistent averaging and improved distance estimation. A simplified factor graph framework is employed to fuse heterogeneous measurements while maintaining computational efficiency suitable for real-time operation on mobile devices. Experimental validation using a robot-based ground-truth reference system demonstrates sub-meter localization accuracy with an average positioning error of approximately 0.40 m. The proposed method provides a low-cost and scalable solution for indoor positioning and navigation applications such as access-controlled environments, exhibitions, and large public venues. Full article

The rapid growth of large-scale Internet of Things (IoT) deployments in urban environments requires accurate and energy-efficient localization methods for low-power wireless devices. In long-range wide-area networks (LoRaWAN), traditional GPS-based positioning is often impractical due to energy consumption constraints and signal propagation challenges in urban areas. This study proposes a hybrid localization system that integrates weighted centroid localization (WCL) with a machine learning (ML) regression model to improve outdoor positioning accuracy. The proposed approach first estimates approximate transmitter coordinates using a physically grounded WCL method based on received signal strength indicator (RSSI) measurements. These initial estimates are subsequently refined by ML models trained to learn nonlinear residual corrections. In addition to random partitioning, a spatial data splitting strategy is proposed and evaluated using a publicly available LoRaWAN dataset. The experimental results demonstrate that the hybrid WCL framework combined with a multilayer perceptron (MLP) significantly outperforms other ML models. The proposed method achieves a mean localization error of 160.47 m and a median error of 73.78 m. Compared to the baseline model, the integration of WCL reduces the mean localization error by approximately 29%, highlighting the effectiveness of incorporating physically interpretable priors into localization models. Full article